Spatial & Temporal Growth of Renewable Energy Capacity in the U.S.
Bella Huang, Cecilia Liang, Isabella Yang, Michelle Du
2025-12-05
Rationale and Research Questions
The U.S. energy system is undergoing a major structural shift as renewable energy, particularly wind and solar, continues to expand across the country. According to the U.S. Energy Information Administration (EIA), renewable energy sources accounted for about 21 percent of total U.S. utility-scale electricity generation in 2023, and that share continues to grow as wind, solar, and other renewables expand their role in the energy mix (EIA, 2024). Although natural gas and petroleum remain the dominant energy sources, the rapid expansion of wind and solar has become one of the most important trends shaping the nation’s electricity mix. This study highlights the regions driving the transition and shows how quickly renewable technologies are reshaping the U.S. electricity system by examining where and when new capacity has been added, using national wind and solar capacity data from 2018–2023 to analyze these trends.
Understanding how renewable capacity is changing spatially and temporally is essential for assessing the pace of the U.S. energy transition. Facility-level datasets from the EIA Form 860 and the EPA’s eGRID provide detailed information on generator technology, capacity (MW), location, and in-service dates, which can be used for analyzing both annual growth patterns and geographic distribution of renewable development.
Research questions
Which U.S. states have experienced the fastest growth in renewable energy capacity from 2018 to 2023, and how does this growth vary by technology?
Is renewable energy capacity growth spatially clustered across the United States, and do different technologies exhibit distinct geographic patterns of expansion?
Does early adoption predict faster renewable expansion?
Dataset Information
Data Source and Collection
This analysis uses data from the Emissions & Generation Resource Integrated Database (eGRID) published by the U.S. Environmental Protection Agency (EPA) for the years 2018–2023. The datasets were obtained from the EPA eGRID archive as a series of Microsoft Excel files corresponding to each reporting year. eGRID compiles electricity generation and capacity data reported annually by U.S. power plants to the U.S. Energy Information Administration (EIA), primarily through EIA Forms 860 and 923, into a standardized national database. To support spatial analysis, this study also incorporates the 2018 Cartographic Boundary File for U.S. counties and county-equivalent units produced by the U.S. Census Bureau. This polygon shapefile provides generalized county boundaries for the entire United States and its territories and is intended for small-scale thematic mapping. The dataset reflects administrative boundaries as of January 1, 2018, is referenced to NAD83, and was used as a spatial framework for aggregating and visualizing plant-level eGRID data.
Data Content and Structure
Each eGRID Excel file contains multiple worksheets, including unit-level, generator-level, plant-level, and state-level tables. This study uses the plant-level (PLNT) table, which provides plant-level information of electricity generation and capacity across the United States, including spatial information, fuel-specific energy contributions, and capacity information. Because each plant record also includes spatial identifiers such as county code and latitude/longitude, this dataset is perfect for linking generation and capacity measures to spatial patterns across regions.
Key variables extracted for analysis include reporting year, plant location, fuel or technology classification, installed capacity (MW), and net generation (MWh). Fuel and technology classifications were used to identify renewable energy facilities, with a focus on wind, solar, and hydroelectric power. Installed capacity represents the maximum rated output of a plant, while net generation reflects actual electricity produced during each reporting year, enabling both spatial and temporal analysis of renewable energy patterns.
Data Wrangling and Preparation
Due to the large size of the raw eGRID files, initial data preparation was performed in Excel prior to analysis in R. Plant-level tables for each year (2018–2023) were converted to CSV format, and only variables required for the analytical objectives were retained due to limited storage on GitHub. To support different analytical goals, two processed versions of the dataset were created and stored in the repository (Data/Raw):
Time-series dataset: retained columns related to reporting year, fuel or technology category, capacity, generation, and other temporal attributes.
Spatial dataset: retained plant location and geographic identifiers, including state, latitude, longitude, plant identifiers, and renewable classification.
Unused variables were removed to reduce file size and accommodate repository storage constraints. Missing values were assessed, particularly in coordinate and fuel-type fields, and records lacking essential information were excluded from aggregation. Data were subsequently summarized by fuel type and location to support both temporal trend analysis and spatial visualization.
Exploratory Analysis
Spatial Exploratory Analysis
1. Explore all US counties.
Figure 1. Interactive Map. County boundaries of the United States displayed from a county-level shapefile. The map allows zooming and panning to explore spatial variation in county size and distribution. Data source: U.S. Census Bureau TIGER/Line Shapefiles
2. 2018 Electricity Plants Locations
Figure 2. Locations of electricity generating plants in the contiguous United States in 2018. Power plants are shown as blue points. Data source: EPA eGRID (2018).
Figure 3. Locations of electricity generating plants in Alaska, United States in 2018. Power plants are shown as blue points. Data source: EPA eGRID (2018).
Figure 4. Locations of electricity generating plants in Hawaii, United States in 2018. Power plants are shown as blue points. Data source: EPA eGRID (2018).
3. Catogorized Electricity Plant Type
Figure 5. Locations of electricity generating plants in the contiguous United States in 2018, categorized by primary fuel type. Data source: EPA eGRID (2018).
Figure 6. Locations of electricity generating plants in Alaksa, USA in 2018, categorized by primary fuel type. Data source: EPA eGRID (2018).
Figure 7. Locations of electricity generating plants in Hawaii, USA in 2018, categorized by primary fuel type. Data source: EPA eGRID (2018).
4. Map the pattern of total generator per state
Figure 8. Interactive Map. Total number of electricity generators by state in the United States (2018). Generator totals are aggregated from plant-level data and visualized using a color gradient. Data sources: EPA eGRID (2018); U.S. Census Bureau TIGER/Line Shapefiles.
5. Total Installed Renewable Capacity by Fuel Type
Graph 1. Total Installed Renewable Capacity by Fuel Type from 2018 to 2023. Different fuel type are shown as different colors. Data source: EPA eGRID (2018-2023).
Analysis
Subsection 1: How have Wind, Solar, and Hydro capacities evolved from 2018–2023?
1. Visualization
Graph 2. Renewable Energy Expansion: Capacity and Generation Growth Across Technologies (2018–2023). The dotted lines indicate the smoothed trend across years for each technology’s capacity and generation additions. Data source: EPA eGRID (2018-2023).
Graph 2 shows how electricity generation from wind, solar, and hydroelectric sources has changed from 2018 to 2023. During this period, solar generation increased the most, reflecting the rapid growth in installed solar capacity and improvements in system efficiency. Wind generation continued to rise as well, though at a steadier pace, very likely resulting from its already large share of renewable output(). In contrast, hydroelectric generation remained fairly stable over these years, which aligns with the limited expansion of hydro infrastructure and the fact that hydro output is more strongly influenced by long-term facility operations and water availability than by new capacity additions.
2. Statistical analysis
| Technology | Slope_MW_per_yr | Std_Error | t_stat | p_value |
|---|---|---|---|---|
| HYDRO | -435.0143 | 146.3012 | -2.973415 | 0.0410053 |
| SOLAR | 12714.7286 | 1135.1832 | 11.200596 | 0.0003618 |
| WIND | 10820.2914 | 797.5774 | 13.566446 | 0.0001709 |
The results from Table 1 show that solar capacity increased the fastest, growing by approximately 12.7 gigawatts per year. And then, wind followed closely with a growth rate at about 10.8 gigawatts per year. Both of these trends are statistically significant with p-values smaller than 0.05, indicating strong and consistent national expansion for solar and wind over the study period. In contrast, hydroelectric capacity shows a small but statistically significant decline of about 435 megawatts per year, reflecting the limited opportunities for new hydropower development and the gradual reduction or aging of existing facilities.
Subsection 2: How have capacity levels changed for each technology? Do different technologies exhibit distinct geographic patterns of expansion?
1. Geographic Distribution Pattern of Power Plant in USA for Wind, Hydro, and Solar Tech
Wind Power
Figure 9. 2018 Wind Power Plants - Mainland USA. Power plants are shown as brown points. Data source: EPA eGRID (2018).
Figure 10. 2018 Wind Power Plants - Alaska. Power plants are shown as brown points. Data source: EPA eGRID (2018).
Figure 11. 2018 Wind Power Plants - Hawaii. Power plants are shown as brown points. Data source: EPA eGRID (2018).
Figure 12. 2023 Wind Power Plants - Mainland USA. Power plants are shown as brown points. Data source: EPA eGRID (2023).
Figure 13. 2023 Wind Power Plants - Alaska. Power plants are shown as brown points. Data source: EPA eGRID (2023).
Figure 14. 2023 Wind Power Plants - Hawaii. Power plants are shown as brown points. Data source: EPA eGRID (2018).
The contrasting distribution of wind power plants between the mainland USA, Alaska, and Hawaii is driven largely by differences in wind resource quality and geography. From figure 9 - 14, it is clear to see that the mainland map shows dense clusters of wind facilities across the Great Plains and upper Midwest: regions characterized by flat terrain and some of the most consistent, high-quality onshore wind resources in the world. These natural conditions create an expansive “wind belt” extending from Texas through the Dakotas, which makes large-scale wind farm development both feasible and economically attractive (U.S. Department of Agriculture, 2022). In contrast, Alaska’s harsh climate limits the possible sites for building turbines. Although there are strong winds along coastal and mountainous areas, much of Alaska’s landscape faces environmental constraints like icing and limited accessible flat land (U.S. Department of Energy, 2021). Wind power plants in Hawaii are few and are concentrated on several major islands where consistent trade winds and suitable locations support wind generation. As a result, wind development in Alaska and Hawaii remains sparse and localized, while the mainland supports more widespread wind power plant installations. Comparing the 2018 (Figure 9-11) and 2023 (Figure 12-14) maps, wind power growth appears to build on existing patterns. In the mainland USA, wind plants remain concentrated in the Great Plains and Texas area, with a noticeable increase in density by 2023. In contrast, Alaska and Hawaii show little change over time, which means ongoing geographic and infrastructure limits on wind expansion in these states are still present.
Hydro Power
Figure 15. 2018 Hydro Power Plants - Mainland USA. Power plants are shown as dark blue points. Data source: EPA eGRID (2018).
Figure 16. 2018 Hydro Power Plants - Alaska. Power plants are shown as dark blue points. Data source: EPA eGRID (2018).
Figure 17. 2018 Hydro Power Plants - Hawaii. Power plants are shown as dark blue points. Data source: EPA eGRID (2018).
Figure 18. 2023 Hydro Power Plants - Mainland USA. Power plants are shown as dark blue points. Data source: EPA eGRID (2023).
Figure 19. 2023 Hydro Power Plants - Alaska. Power plants are shown as dark blue points. Data source: EPA eGRID (2023).
Figure 20. 2023 Hydro Power Plants - Hawaii. Power plants are shown as dark blue points. Data source: EPA eGRID (2023).
Hydroelectric plant distribution in the mainland U.S. and Alaska is shaped primarily by geography and hydrological conditions (figure 15-20). In the mainland, hydro facilities cluster in the Pacific Northwest, the Sierra Nevada, and parts of the Appalachians: regions with steep terrain, major river systems, and abundant water flow that make large-scale hydropower feasible (U.S. Department of Energy, 1998). In contrast, Alaska’s hydro plants are concentrated along the southeastern coastline, where heavy rainfall and mountains create ideal small- to medium-scale hydro sites. Much of Alaska’s interior lacks sufficient river gradients or water volume, limiting hydropower development. The same goes for Hawaii. With limited access to large rivers, hydro power plants are not a popular choice in both Hawaii and Alaska. Compared with 2018 (figure 15-17), the 2023 (figure 18-20) hydro power map shows very little change in overall spatial distribution across the mainland United States. Hydroelectric plants remain concentrated in the same water-rich regions, particularly the Pacific Northwest, California, and parts of the Northeast and Appalachians. This can be explained by the unique characteristics of hydro power. Currently, all suitable locations in the United States capable of hosting hydropower projects have already been developed for hydroelectric generation, so there has been no significant change over the past five years.
Solar Power
Figure 21. 2018 Solar Power Plants - Mainland USA. Power plants are shown as orange points. Data source: EPA eGRID (2018).
Figure 22. 2018 Solar Power Plants - Alaska. Power plants are shown as orange points. Data source: EPA eGRID (2018).
Figure 23. 2018 Solar Power Plants - Mainland USA. Power plants are shown as orange points. Data source: EPA eGRID (2018).
Figure 24. 2023 Solar Power Plants - Mainland USA. Power plants are shown as dark blue points. Data source: EPA eGRID (2023).
Figure 25. 2023 Solar Power Plants - Alaska. Power plants are shown as orange points. Data source: EPA eGRID (2023).
Figure 26. 2023 Solar Power Plants - Hawaii. Power plants are shown as orange points. Data source: EPA eGRID (2023).
The distribution of solar electricity plants in the mainland United States reflects the strong relationship between solar development and solar resource availability and climate. In the mainland U.S. map (figure 21), solar facilities are heavily concentrated in the Southwest, including California, Arizona, Nevada, New Mexico, and Texas: regions with some of the highest solar irradiance in the country and large areas of open land well suited for solar farms. Additional clusters appear in the Southeast and Mid-Atlantic, where supportive policies and growing energy demand have encouraged solar expansion despite slightly lower sunlight levels (National Renewable Energy Laboratory. 2018). In contrast, Alaska has no utility-scale solar plants in 2018, largely because the state receives far fewer hours of sunlight, especially in winter, and experiences long periods of low solar angle and cloudiness. Compared to 2018, the 2023 (figure 24) solar power map shows clear and widespread growth across the mainland United States. Solar plants are no longer concentrated mainly in the Southwest and California; instead, there is much denser development in the Southeast, Texas, and parts of the Midwest and Northeast. Also, there is one solar panel spotted in Alaska in 2023 (figure 26). Comparing the 2018 (figure 23) and 2023 (figure 26) solar maps for Hawaii shows gradual steady growth. Solar plants remain concentrated on the main islands, particularly Oʻahu and Maui, with more installations on the figure 26. Surprisingly, in 2023, Alaska gained its first solar power plant in the state, symbolizing the beginning of a new phase in renewable energy development (figure 25).
2. Statistical and Time Series Analysis
| Technology | Kendall_tau | p_value | signif |
|---|---|---|---|
| HYDRO | -0.467 | 0.2597 | |
| SOLAR | 1.000 | 0.0085 | ** |
| WIND | 1.000 | 0.0085 | ** |
Null hypothesis (H₀): The installed capacity shows no monotonic trend over time (τ = 0). Alternative hypothesis (H₁): The installed capacity follows a significant monotonic trend over time (τ ≠ 0). The Mann–Kendall results presented in Table 2 showed that there are clear differences in long-term capacity trends between renewable technologies of Hydro, Solar, and Wind. Solar and wind both exhibit strong positive monotonic trends as they strongly rejects the null hypothesis with Kendall’s τ equal to 1.00 and statistically significant p-values (p = 0.0085). Thus, there is a consistent increasing trend in installed capacity.
On the other hand, the test result for hydroelectric capacity fails to reject the null hypothesis, with only a modest negative τ value (τ = –0.467). What’s more, its p-value (p = 0.2597>0.1) is not statistically significant, suggesting that there is not any statistically meaningful long-term trend.
Subsection 3: Which states contribute most to renewable capacity growth?
1. Spatial presentation
To complement the national and technology-wide trends, we next examine how renewable energy development is distributed across U.S. states. The deployment of solar, wind, and hydro capacity is far from uniform; instead, each technology exhibits a distinct geographical concentration shaped by resource availability, market conditions, and policy frameworks. Identifying the top-producing states allows us to understand where each technology has taken hold and how leadership positions have evolved over time. The Top 5 rankings for 2018–2023 reveal clear and persistent patterns: California dominates solar, Washington remains the center of U.S. hydropower, and Texas consistently leads wind development by a substantial margin. These states not only anchor their respective technologies but also influence national trends through their scale, investment activity, and infrastructure advantages. Building on these rankings, the following sections explore each leading state in greater depth, analyzing how capacity additions, generation trends, and plant expansion patterns unfold within their dominant technology sectors.
Figure 27. Spatial Distribution of California Power Plants by Primary Fuel Type, 2023. Power plants are shown as colorful points. Data source: EPA eGRID (2023).
Figure 28. Spatial Distribution of Washington Power Plants by Primary Fuel Type, 2023. Power plants are shown as colorful points. Data source: EPA eGRID (2023).
Figure 29. Spatial Distribution of Texas Power Plants by Primary Fuel Type, 2023. Power plants are shown as colorful points. Data source: EPA eGRID (2023).
2. State Zoom-in
| rank | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|---|
| 1 | CA | CA | CA | CA | CA | CA |
| 2 | NC | NC | TX | TX | TX | TX |
| 3 | AZ | AZ | NC | NC | FL | FL |
| 4 | TX | TX | FL | FL | NC | NC |
| 5 | FL | FL | AZ | NV | GA | AZ |
| rank | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|---|
| 1 | WA | WA | WA | WA | WA | WA |
| 2 | CA | CA | CA | CA | CA | CA |
| 3 | OR | OR | OR | OR | OR | OR |
| 4 | NY | NY | NY | NY | NY | NY |
| 5 | SC | SC | SC | TN | TN | SC |
| rank | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|---|
| 1 | TX | TX | TX | TX | TX | TX |
| 2 | IA | IA | IA | IA | IA | IA |
| 3 | OK | OK | OK | OK | OK | OK |
| 4 | CA | KS | KS | KS | KS | KS |
| 5 | KS | CA | IL | IL | IL | IL |
Tables 3–5 summarize the top five states contributing to wind, solar, and hydroelectric capacity additions from 2018 to 2023. The preceding national‐level analysis demonstrates that renewable energy expansion in the United States is highly heterogeneous across technologies and regions. While wind, solar, and hydro each show distinct national trajectories—characterized respectively by rapid expansion, sustained acceleration, and long-term stagnation—these aggregate patterns mask substantial state-level variation in how and where growth is occurring. Technologies develop unevenly across the country because their deployment depends on local resource availability, state policy, infrastructure constraints, and market conditions.
For solar capacity, California consistently ranks first every year, reflecting the state’s long-standing policy incentives and strong solar resource potential (Table 3). North Carolina and Arizona follow closely, though their ranking shifts slightly over time. To be noticed, Texas rises into the top three by 2020, indicating that the state is also having a rapid solar deployment in recent years. The map (figure 27) also proves this point. Solar power dominates the map, with especially dense clusters across Southern California and the Central Valley.
Hydroelectric capacity, on the other hand, remains almost entirely dominated by Pacific Northwest and East Coast states (Table 2). Washington is the top hydro state across all six years, with California, Oregon, and New York consistently filling the next three positions. The stability of these rankings proves that hydropower is strongly dependent on established infrastructures and geographic constraints, with relatively few new installations occurring within the study period. The map (figure 28) shows a similar pattern. Hydropower clearly dominates Washington’s electricity landscape, with dense clusters of hydro plants spread across the state, particularly along major river systems.
Wind capacity growth is concentrated in the central United States. As shown in Table 3, Texas leads wind capacity additions by a wide margin every year, maintaining its position as the largest wind-producing state in the U.S. Iowa and Oklahoma remain strong contributors, holding the second and third positions throughout the period. Kansas and California round out the top five, with Illinois entering the ranking from 2020 onward. These trends support the hypothesis that wind energy development is anchored in states with large available land areas, consistent wind resources, and established transmission networks. In figure 29, Wind power stands out as a dominant feature in Texas, with dense clusters of wind plants concentrated in the Panhandle, West Texas, and central regions.
To better understand the drivers underlying the national trends, we examine states that are dominant contributors within each technology segment. Texas, Washington, and California provide particularly informative examples because each state plays an outsized role in one major renewable technology and exhibits a characteristic deployment profile:
3a. Wind
Across the results graph of the Texas Wind total wind generation, installed wind capacity and number of wind plants, Texas shows a consistent and accelerated growth over the 2018-2023 period. The wind number of wind plants in Texas increased steadily from around 160 plants in 2018 to over 210 by 2023. This reflects a robust pipeline of new project development and repowering efforts. Growth exceeds the linear trend in most years, indicating particularly strong expansion in 2019- 2022. With the increase of the number of wind plants, the Wind capacity rose from roughly 24-25 GW in 2018 to over 40 GW in 2023, leading to a increase of around 65% of previous number. With the growth trajectory aligned with and even exceed the linear trend, the result suggested a continued investment in larger and more efficient wind farms. Capacity additions accelerated around 2021 and 2022, consistent with the timing of federal tax incentives and completion of major transmission upgrades such CREZ corridors, promoting utility- scale wind development and to ensure adequate transmission to bring the new power into the state’s grid. Wind generation in Texas grew substantially faster than capacity, climbing from 75-80 TWh in 2018 to more than 115 TWh in 2023, reflecting a improvement in turbine efficiency, a higher average capacity factors, a reduced curtailment due to expanded transmission.
Figure 30. Annual installed Wind capacity in Texas from 2018 to 2023.
The solid line represents observed total nameplate capacity, while the dashed line shows a fitted linear trend. The figure illustrates year-to-year changes in infrastructure expansion and highlights the overall direction and magnitude of long-term growth.
Figure 31 . Annual Wind Generation in Texas from 2018 to 2023.
The solid line represents observed total generation, while the dashed line shows a fitted linear trend. The figure illustrates year-to-year changes in infrastructure expansion and highlights the overall direction and magnitude of long-term growth.
Figure 32. Annual number of Wind plants in Texas from 2018 to 2023.
The solid line shows observed plant counts, while the dashed linear trend highlights the long-term direction of development. This figure illustrates how facility expansion (or contraction) unfolded over time and whether growth patterns align with overall capacity and generation trends.
Graph 3. Three-Panel Trend Analysis of U.S. Wind Capacity (2018–2023). The panels show raw annual capacity values, LOESS-estimated trends with linear fits, and residual structure. Trend statistics (LM slope and Mann–Kendall τ) are displayed in the footer.
The wind capacity data show a statistically significant increase from 2018 to 2023, with no declines across the period. Both the LOESS trend line and the linear fit indicate a clear upward trajectory, and the statistical tests confirm this pattern. The linear model estimates an average increase of about 10.8 GW per year (p = 0.000171), and the Mann–Kendall test also detects a significant monotonic trend (τ = 1.00, p = 0.0085). The residuals are minimal, indicating that the trend model fits well.
3b. Solar
Solar capacity shows a steady and substantial increase from 2018 to 2023. Both the LOESS trend and the linear fit indicate a strong upward trajectory, and the statistical tests confirm this pattern. The linear model estimates an average increase of about 12.7 GW per year (p = 0.000362), and the Mann–Kendall test identifies a significant monotonic upward trend (τ = 1.00, p = 0.0085). The residuals are minimal, suggesting the trend model fits well. Overall, solar capacity grew rapidly and consistently during this period.
Figure 33. Annual installed Solar capacity in California from 2018 to 2023.
The solid line represents observed total nameplate capacity, while the dashed line shows a fitted linear trend. The figure illustrates year-to-year changes in infrastructure expansion and highlights the overall direction and magnitude of long-term growth.
Figure 34. Annual installed Solar Generation in California from 2018 to 2023.
The solid line represents observed total Generation, while the dashed line shows a fitted linear trend. The figure illustrates year-to-year changes in infrastructure expansion and highlights the overall direction and magnitude of long-term growth.
Figure 35. Annual number of Solar plants in California from 2018 to 2023.
The solid line shows observed plant counts, while the dashed linear trend highlights the long-term direction of development. This figure illustrates how facility expansion (or contraction) unfolded over time and whether growth patterns align with overall capacity and generation trends.
3c. Hydro
Hydro capacity remains mostly stable from 2018 to 2023, with slight year-to-year fluctuations but no clear upward trend. The linear model suggests a small annual decline of about 435 MW per year (p = 0.041), but the Mann–Kendall test does not detect a significant monotonic trend (τ = −0.467, p = 0.26). The discrepancy reflects minor variability rather than a strong directional shift. Overall, hydro capacity appears essentially flat over the study period, with no meaningful long-term increase or decrease.
Figure 36. Annual installed Hydro capacity in Washington from 2018 to 2023.
Figure 37. Annual installed Hydro Generation in Washington from 2018 to 2023.
Figure 38. Annual number of Hydro plants in Washington from 2018 to 2023.
The solid line shows observed plant counts, while the dashed linear trend highlights the long-term direction of development. This figure illustrates how facility expansion (or contraction) unfolded over time and whether growth patterns align with overall capacity and generation trends.
4. Do early adopters have faster renewable expansion?
| Technology | Slope | Std. Error | t value | p value | signif | 95% CI lower | 95% CI upper | R_squared |
|---|---|---|---|---|---|---|---|---|
| Solar | 0.99 | 0.14 | 6.96 | 0.0000 | *** | 0.71 | 1.28 | 0.502 |
| Wind | 0.59 | 0.03 | 20.25 | 0.0000 | *** | 0.53 | 0.65 | 0.895 |
| Hydro | -0.03 | 0.00 | -8.39 | 0.0000 | *** | -0.04 | -0.02 | 0.595 |
| Note: Signif.: * p < 0.05, ** p < 0.01, *** p < 0.001 |
In this analysis, early adoption is defined as the baseline renewable capacity a state possessed in 2018, the first year of our study period. States with higher 2018 capacity in a given technology are considered early adopters of that technology. Then, a regression analysis was conducted using this definition to test whether baseline capacity predicts capacity growth from 2018 to 2023.
The regression results indicate that early adoption plays a meaningful role in predicting renewable energy growth for solar and wind technologies. States with higher baseline solar capacity in 2018 tended to add substantially more capacity by 2023, as shown by the strong positive slope (0.99) and a highly significant p-value. Wind shows a similar pattern, with a positive slope (0.59) and strong statistical significance, suggesting that states with larger initial wind investments continued to expand more rapidly. In contrast, hydro displays a small negative slope (-0.03), which, although statistically significant, likely reflects the limited opportunities for large-scale hydro expansion due to geographic and environmental constraints.
Overall, the analysis suggests that solar and wind development exhibit momentum effects—states that started ahead tended to grow faster—while hydro capacity remained relatively stable or slightly declined over time. The regression results show strong evidence of momentum effects for both solar and wind. States with higher initial solar capacity added significantly more capacity over the study period, as indicated by a near one-to-one positive slope (0.99, p < 0.001). Wind capacity displays a similar but slightly weaker pattern, with a positive slope (0.59, p < 0.001), suggesting that states with larger early wind investments continued to scale up more rapidly. In contrast, hydro exhibits a small negative slope (–0.03), likely reflecting the physical and geographic limits on expanding hydropower systems rather than meaningful declines in development activity.
Summary and Conclusions
This study examined spatial and temporal patterns of renewable energy development in the United States from 2018 to 2023 using plant-level data from the EPA’s eGRID database and associated geographic datasets. By integrating time-series trends, spatial analysis, statistical modeling, and cross-state comparisons, the analysis characterizes recent development of wind, solar, and hydropower. It also examines which regions are leading the energy transition, the spatial distribution characteristics of technology clusters, and how early adoption has shaped today’s energy landscape.
Solar and wind exhibit clear, statistically significant growth over the study period, while hydroelectric capacity remains essentially stable. Solar expanded at the fastest rate, increasing by approximately 12.7 GW per year, followed by wind at 10.8 GW per year, and both supported by strong linear and Mann–Kendall results. Hydroelectric capacity showed no meaningful expansion and instead a modest long-term decline of roughly 435 MW per year, reflecting geographic, infrastructural, and regulatory constraints. Generation trends align with these patterns: wind and solar production increased steadily alongside capacity growth, whereas hydroelectric generation fluctuated substantially due to hydrologic variability and infrastructural constraints.
Spatial analyses reveal that renewable expansion is highly spatially concentrated. Solar growth is most pronounced in the Southwest and Southeast, while wind development remains concentrated across the Great Plains and Texas. Hydroelectric facilities remain clustered in the Pacific Northwest and Northeast, with no evidence of spatial expansion. Comparisons between 2018 and 2023 indicate that new installations primarily reinforce existing geographic clusters rather than establish new ones.
Early adoption strongly predicts continued growth for solar and wind technologies. States that began with strong solar or wind portfolios in 2018 tended to experience the fastest increases through 2023. This relationship does not extend to hydropower, where expansion is limited.
State-level case studies highlight the technology-specific drivers propelling these trends. California consistently leads solar development, Texas dominates wind capacity and generation, and Washington remains the center of U.S. hydropower. These leadership positions remain stable throughout the study period, which reflects long-standing resource advantages, policy support, and pre-existing infrastructure. Case studies illustrate technology-specific drivers, including Texas’s transmission investments, such as the CREZ system, Washington’s structurally fixed hydroelectric system, and California’s policy-driven solar expansion.
Overall, the U.S. renewable energy landscape from 2018 to 2023 is characterized by technological divergence, persistent spatial clustering, and path-dependent growth. Solar and wind continue to expand rapidly within established regional cores, while hydropower remains largely static. States that were leaders at the start of the study period—most notably California, Texas, and Washington—continue to dominate their respective technologies, reinforcing regional specialization in the U.S. energy transition.
References
U.S. Energy Information Administration (EIA). 2024. “What Is U.S. Electricity Generation by Energy Source?” https://www.eia.gov/tools/faqs/faq.php?id=427&t=3
Solar Energy in the Arctic: A Case Study of Northwest Alaska.
Electricity Generation from Renewables in the United States: Resource Potential, Current Usage, Technical Status, Challenges, Strategies, Policies, and Future Directions.
U.S. Department of Energy. 2023. Hydropower Market Reports 2023. Office of Energy Efficiency & Renewable Energy.
Our World in Data. n.d. “Renewable Energy.” https://ourworldindata.org/renewable-energy
U.S. Department of Agriculture, Economic Research Service. 2022. “Wind Power Development Has Grown Rapidly in the Central and Midwest Regions of the United States.” Charts of Note. https://ers.usda.gov/data-products/charts-of-note/chart-detail/?chartId=109244
U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. 2021. “Distributed Wind Energy Brings Value to Remote and Rural Communities.” https://www.energy.gov/eere/wind/articles/distributed-wind-energy-brings-value-remote-and-rural-communities
U.S. Department of Energy. 1998. “Hydropower Resource Assessment and Characterization.” Office of Energy Efficiency & Renewable Energy. https://www1.eere.energy.gov/does-water-10430
National Renewable Energy Laboratory (NREL). n.d. “United States Solar Resource Maps.” https://www.nrel.gov/gis/solar-resource-maps.html
Baker Institute for Public Policy. n.d. Texas CREZ Lines: How Stakeholders Shape Major Energy Infrastructure Projects. https://www.bakerinstitute.org/research/texas-crez-lines-how-stakeholders-shape-major-energy-infrastructure-projects
University of Florida, Warrington College of Business. n.d. “What Is Curtailment? An Electricity Market Expert Explains.” https://warrington.ufl.edu/news/what-is-curtailment-an-electricity-market-expert-explains/
California Energy Commission. 2024. Scaling Solar for Small and Medium Commercial Buildings. https://www.energy.ca.gov/publications/2024/scaling-solar-small-and-medium-commercial-buildings
U.S. Energy Information Administration (EIA). 2023. “Today in Energy: Growth of Renewable Electricity in the United States.” https://www.eia.gov/todayinenergy/detail.php?id=60341
U.S. Energy Information Administration. 2024. What Is U.S. Electricity Generation by Energy Source? https://www.eia.gov/tools/faqs/faq.php?id=427&t=3